Overview

Brought to you by YData

Dataset statistics

Number of variables27
Number of observations217140
Missing cells283641
Missing cells (%)4.8%
Duplicate rows47971
Duplicate rows (%)22.1%
Total size in memory81.0 MiB
Average record size in memory391.2 B

Variable types

Text2
Numeric13
Categorical10
Unsupported1
DateTime1

Alerts

n_hog has constant value "1" Constant
Dataset has 47971 (22.1%) duplicate rowsDuplicates
p1 is highly overall correlated with p3High correlation
p2 is highly overall correlated with p4High correlation
p3 is highly overall correlated with p1High correlation
p4 is highly overall correlated with p2High correlation
h_mud is highly imbalanced (87.6%) Imbalance
p14 is highly imbalanced (61.9%) Imbalance
p15 is highly imbalanced (52.6%) Imbalance
i_per has 44941 (20.7%) missing values Missing
ing has 162190 (74.7%) missing values Missing
fch_def has 38255 (17.6%) missing values Missing
year has 38255 (17.6%) missing values Missing
con is highly skewed (γ1 = -27.51120919) Skewed
i_per is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2024-11-06 23:51:16.290605
Analysis finished2024-11-06 23:52:12.610952
Duration56.32 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

fol
Text

Distinct219
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.4 MiB
2024-11-06T16:52:13.119592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1302840
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11B167
2nd row11B167
3rd row11B167
4th row11B167
5th row11B167
ValueCountFrequency (%)
11a201 1818
 
0.8%
11a208 1691
 
0.8%
12b157 1687
 
0.8%
11a205 1686
 
0.8%
11a218 1654
 
0.8%
12a177 1640
 
0.8%
11b155 1632
 
0.8%
12a164 1626
 
0.7%
11b211 1626
 
0.7%
12b165 1623
 
0.7%
Other values (209) 200457
92.3%
2024-11-06T16:52:13.887538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 512600
39.3%
2 177953
 
13.7%
B 115185
 
8.8%
A 101955
 
7.8%
7 74653
 
5.7%
3 62942
 
4.8%
6 55450
 
4.3%
8 48370
 
3.7%
5 47776
 
3.7%
0 40373
 
3.1%
Other values (2) 65583
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1302840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 512600
39.3%
2 177953
 
13.7%
B 115185
 
8.8%
A 101955
 
7.8%
7 74653
 
5.7%
3 62942
 
4.8%
6 55450
 
4.3%
8 48370
 
3.7%
5 47776
 
3.7%
0 40373
 
3.1%
Other values (2) 65583
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1302840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 512600
39.3%
2 177953
 
13.7%
B 115185
 
8.8%
A 101955
 
7.8%
7 74653
 
5.7%
3 62942
 
4.8%
6 55450
 
4.3%
8 48370
 
3.7%
5 47776
 
3.7%
0 40373
 
3.1%
Other values (2) 65583
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1302840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 512600
39.3%
2 177953
 
13.7%
B 115185
 
8.8%
A 101955
 
7.8%
7 74653
 
5.7%
3 62942
 
4.8%
6 55450
 
4.3%
8 48370
 
3.7%
5 47776
 
3.7%
0 40373
 
3.1%
Other values (2) 65583
 
5.0%

ent
Real number (ℝ)

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.686824
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2024-11-06T16:52:14.124903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q19
median15
Q320
95-th percentile30
Maximum32
Range31
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.8373219
Coefficient of variation (CV)0.49961176
Kurtosis-0.61108292
Mean15.686824
Median Absolute Deviation (MAD)6
Skewness0.2040933
Sum3406237
Variance61.423614
MonotonicityNot monotonic
2024-11-06T16:52:14.348305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
19 24375
 
11.2%
14 23380
 
10.8%
9 21111
 
9.7%
15 18158
 
8.4%
11 8850
 
4.1%
1 5750
 
2.6%
21 5747
 
2.6%
5 5533
 
2.5%
17 5272
 
2.4%
20 5060
 
2.3%
Other values (22) 93904
43.2%
ValueCountFrequency (%)
1 5750
 
2.6%
2 4502
 
2.1%
3 3444
 
1.6%
4 3895
 
1.8%
5 5533
 
2.5%
6 3581
 
1.6%
7 4812
 
2.2%
8 3839
 
1.8%
9 21111
9.7%
10 4914
 
2.3%
ValueCountFrequency (%)
32 4210
1.9%
31 4459
2.1%
30 4997
2.3%
29 4140
1.9%
28 4088
1.9%
27 3825
1.8%
26 4213
1.9%
25 4853
2.2%
24 4262
2.0%
23 4063
1.9%

con
Real number (ℝ)

Skewed 

Distinct815
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40295.016
Minimum22251
Maximum41420
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2024-11-06T16:52:14.752224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum22251
5-th percentile40027
Q140151
median40271
Q340391
95-th percentile40598
Maximum41420
Range19169
Interquartile range (IQR)240

Descriptive statistics

Standard deviation491.43832
Coefficient of variation (CV)0.012196007
Kurtosis1018.0357
Mean40295.016
Median Absolute Deviation (MAD)120
Skewness-27.511209
Sum8.7496598 × 109
Variance241511.62
MonotonicityNot monotonic
2024-11-06T16:52:14.993579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40269 1227
 
0.6%
40271 1161
 
0.5%
40277 1157
 
0.5%
40403 1060
 
0.5%
40279 975
 
0.4%
40406 946
 
0.4%
40402 909
 
0.4%
40273 895
 
0.4%
40305 888
 
0.4%
40405 888
 
0.4%
Other values (805) 207034
95.3%
ValueCountFrequency (%)
22251 64
 
< 0.1%
22256 25
 
< 0.1%
22259 33
 
< 0.1%
40001 388
0.2%
40002 402
0.2%
40003 333
0.2%
40004 230
0.1%
40005 426
0.2%
40006 301
0.1%
40007 267
0.1%
ValueCountFrequency (%)
41420 41
 
< 0.1%
41419 56
< 0.1%
41418 44
 
< 0.1%
41417 48
< 0.1%
41415 32
 
< 0.1%
41412 116
0.1%
41411 65
< 0.1%
41409 56
< 0.1%
41408 21
 
< 0.1%
41406 39
 
< 0.1%

v_sel
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 MiB
3
55204 
4
54012 
1
54005 
2
53919 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters217140
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 55204
25.4%
4 54012
24.9%
1 54005
24.9%
2 53919
24.8%

Length

2024-11-06T16:52:15.235933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T16:52:15.453350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 55204
25.4%
4 54012
24.9%
1 54005
24.9%
2 53919
24.8%

Most occurring characters

ValueCountFrequency (%)
3 55204
25.4%
4 54012
24.9%
1 54005
24.9%
2 53919
24.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 217140
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 55204
25.4%
4 54012
24.9%
1 54005
24.9%
2 53919
24.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 217140
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 55204
25.4%
4 54012
24.9%
1 54005
24.9%
2 53919
24.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 217140
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 55204
25.4%
4 54012
24.9%
1 54005
24.9%
2 53919
24.8%

n_hog
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 MiB
1
217140 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters217140
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 217140
100.0%

Length

2024-11-06T16:52:15.666778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T16:52:15.848293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 217140
100.0%

Most occurring characters

ValueCountFrequency (%)
1 217140
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 217140
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 217140
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 217140
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 217140
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 217140
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 217140
100.0%

h_mud
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 MiB
0
208895 
1
 
7683
2
 
522
3
 
40

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters217140
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 208895
96.2%
1 7683
 
3.5%
2 522
 
0.2%
3 40
 
< 0.1%

Length

2024-11-06T16:52:16.031803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T16:52:16.229274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 208895
96.2%
1 7683
 
3.5%
2 522
 
0.2%
3 40
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 208895
96.2%
1 7683
 
3.5%
2 522
 
0.2%
3 40
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 217140
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 208895
96.2%
1 7683
 
3.5%
2 522
 
0.2%
3 40
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 217140
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 208895
96.2%
1 7683
 
3.5%
2 522
 
0.2%
3 40
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 217140
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 208895
96.2%
1 7683
 
3.5%
2 522
 
0.2%
3 40
 
< 0.1%

i_per
Unsupported

Missing  Rejected  Unsupported 

Missing44941
Missing (%)20.7%
Memory size9.1 MiB

ing
Real number (ℝ)

Missing 

Distinct387
Distinct (%)0.7%
Missing162190
Missing (%)74.7%
Infinite0
Infinite (%)0.0%
Mean3419.1977
Minimum25
Maximum250000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2024-11-06T16:52:16.454671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile500
Q11200
median1900
Q33600
95-th percentile10000
Maximum250000
Range249975
Interquartile range (IQR)2400

Descriptive statistics

Standard deviation5889.1451
Coefficient of variation (CV)1.7223763
Kurtosis391.8691
Mean3419.1977
Median Absolute Deviation (MAD)900
Skewness13.519539
Sum1.8788491 × 108
Variance34682030
MonotonicityNot monotonic
2024-11-06T16:52:16.716969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1500 4728
 
2.2%
2000 4644
 
2.1%
1000 4168
 
1.9%
1200 3949
 
1.8%
3000 3048
 
1.4%
2500 2231
 
1.0%
5000 2041
 
0.9%
4000 1920
 
0.9%
1800 1751
 
0.8%
800 1636
 
0.8%
Other values (377) 24834
 
11.4%
(Missing) 162190
74.7%
ValueCountFrequency (%)
25 1
 
< 0.1%
30 2
 
< 0.1%
40 2
 
< 0.1%
50 27
< 0.1%
60 5
 
< 0.1%
70 4
 
< 0.1%
75 1
 
< 0.1%
80 9
 
< 0.1%
90 3
 
< 0.1%
99 2
 
< 0.1%
ValueCountFrequency (%)
250000 5
 
< 0.1%
200000 2
 
< 0.1%
150009 1
 
< 0.1%
150000 2
 
< 0.1%
100000 17
< 0.1%
95000 1
 
< 0.1%
80000 9
< 0.1%
75000 6
 
< 0.1%
70001 1
 
< 0.1%
70000 12
< 0.1%

mpio
Real number (ℝ)

Distinct91
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.076932
Minimum1
Maximum553
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2024-11-06T16:52:16.950346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median26
Q350
95-th percentile120
Maximum553
Range552
Interquartile range (IQR)44

Descriptive statistics

Standard deviation52.167613
Coefficient of variation (CV)1.2699978
Kurtosis18.384848
Mean41.076932
Median Absolute Deviation (MAD)20
Skewness3.390494
Sum8919445
Variance2721.4598
MonotonicityNot monotonic
2024-11-06T16:52:17.189705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 15213
 
7.0%
5 12233
 
5.6%
1 9809
 
4.5%
6 9721
 
4.5%
30 9113
 
4.2%
4 8539
 
3.9%
20 8487
 
3.9%
101 8279
 
3.8%
2 7738
 
3.6%
17 6976
 
3.2%
Other values (81) 121032
55.7%
ValueCountFrequency (%)
1 9809
4.5%
2 7738
3.6%
3 6256
2.9%
4 8539
3.9%
5 12233
5.6%
6 9721
4.5%
7 6957
3.2%
8 1718
 
0.8%
9 1620
 
0.7%
10 3288
 
1.5%
ValueCountFrequency (%)
553 104
 
< 0.1%
403 27
 
< 0.1%
399 314
 
0.1%
390 533
 
0.2%
385 631
 
0.3%
350 176
 
0.1%
293 92
 
< 0.1%
193 3839
1.8%
157 72
 
< 0.1%
140 260
 
0.1%

ageb
Text

Distinct3235
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size11.2 MiB
2024-11-06T16:52:17.889832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.968145
Min length4

Characters and Unicode

Total characters1078783
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row025-1
2nd row025-1
3rd row025-1
4th row025-1
5th row025-1
ValueCountFrequency (%)
025-1 393
 
0.2%
021-a 382
 
0.2%
065-2 369
 
0.2%
031-0 332
 
0.2%
039-7 329
 
0.2%
036-3 328
 
0.2%
039-3 328
 
0.2%
013-3 305
 
0.1%
161-6 296
 
0.1%
141-6 296
 
0.1%
Other values (3225) 213782
98.5%
2024-11-06T16:52:18.769480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 210223
19.5%
0 141443
13.1%
1 124512
11.5%
2 97451
9.0%
3 86249
8.0%
4 77667
 
7.2%
5 73357
 
6.8%
6 66129
 
6.1%
7 63161
 
5.9%
8 59250
 
5.5%
Other values (2) 79341
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1078783
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 210223
19.5%
0 141443
13.1%
1 124512
11.5%
2 97451
9.0%
3 86249
8.0%
4 77667
 
7.2%
5 73357
 
6.8%
6 66129
 
6.1%
7 63161
 
5.9%
8 59250
 
5.5%
Other values (2) 79341
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1078783
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 210223
19.5%
0 141443
13.1%
1 124512
11.5%
2 97451
9.0%
3 86249
8.0%
4 77667
 
7.2%
5 73357
 
6.8%
6 66129
 
6.1%
7 63161
 
5.9%
8 59250
 
5.5%
Other values (2) 79341
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1078783
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 210223
19.5%
0 141443
13.1%
1 124512
11.5%
2 97451
9.0%
3 86249
8.0%
4 77667
 
7.2%
5 73357
 
6.8%
6 66129
 
6.1%
7 63161
 
5.9%
8 59250
 
5.5%
Other values (2) 79341
 
7.4%

fch_def
Date

Missing 

Distinct468
Distinct (%)0.3%
Missing38255
Missing (%)17.6%
Memory size1.7 MiB
Minimum2018-01-03 00:00:00
Maximum2022-12-12 00:00:00
2024-11-06T16:52:19.005847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:19.260192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

p1
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2210509
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2024-11-06T16:52:19.444699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.78781768
Coefficient of variation (CV)0.24458405
Kurtosis-0.19985358
Mean3.2210509
Median Absolute Deviation (MAD)1
Skewness-0.11988718
Sum699419
Variance0.6206567
MonotonicityNot monotonic
2024-11-06T16:52:19.594298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 100944
46.5%
4 72142
33.2%
2 34363
 
15.8%
5 7373
 
3.4%
1 2296
 
1.1%
6 22
 
< 0.1%
ValueCountFrequency (%)
1 2296
 
1.1%
2 34363
 
15.8%
3 100944
46.5%
4 72142
33.2%
5 7373
 
3.4%
6 22
 
< 0.1%
ValueCountFrequency (%)
6 22
 
< 0.1%
5 7373
 
3.4%
4 72142
33.2%
3 100944
46.5%
2 34363
 
15.8%
1 2296
 
1.1%

p2
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9063738
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2024-11-06T16:52:19.745869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.94498544
Coefficient of variation (CV)0.32514243
Kurtosis0.46240853
Mean2.9063738
Median Absolute Deviation (MAD)1
Skewness0.58539957
Sum631090
Variance0.89299747
MonotonicityNot monotonic
2024-11-06T16:52:19.933390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 85853
39.5%
2 72394
33.3%
4 42206
19.4%
5 7193
 
3.3%
1 6602
 
3.0%
6 2892
 
1.3%
ValueCountFrequency (%)
1 6602
 
3.0%
2 72394
33.3%
3 85853
39.5%
4 42206
19.4%
5 7193
 
3.3%
6 2892
 
1.3%
ValueCountFrequency (%)
6 2892
 
1.3%
5 7193
 
3.3%
4 42206
19.4%
3 85853
39.5%
2 72394
33.3%
1 6602
 
3.0%

p3
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.162591
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2024-11-06T16:52:20.090948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.76360676
Coefficient of variation (CV)0.24144974
Kurtosis-0.052884322
Mean3.162591
Median Absolute Deviation (MAD)0
Skewness-0.072139686
Sum686725
Variance0.58309528
MonotonicityNot monotonic
2024-11-06T16:52:20.289414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 109177
50.3%
4 64447
29.7%
2 35526
 
16.4%
5 5486
 
2.5%
1 2420
 
1.1%
6 84
 
< 0.1%
ValueCountFrequency (%)
1 2420
 
1.1%
2 35526
 
16.4%
3 109177
50.3%
4 64447
29.7%
5 5486
 
2.5%
6 84
 
< 0.1%
ValueCountFrequency (%)
6 84
 
< 0.1%
5 5486
 
2.5%
4 64447
29.7%
3 109177
50.3%
2 35526
 
16.4%
1 2420
 
1.1%

p4
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8578797
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2024-11-06T16:52:20.461952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.92279829
Coefficient of variation (CV)0.32289613
Kurtosis0.60494927
Mean2.8578797
Median Absolute Deviation (MAD)1
Skewness0.62982108
Sum620560
Variance0.85155669
MonotonicityNot monotonic
2024-11-06T16:52:20.638481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 86961
40.0%
2 76175
35.1%
4 38603
17.8%
1 6668
 
3.1%
5 6151
 
2.8%
6 2582
 
1.2%
ValueCountFrequency (%)
1 6668
 
3.1%
2 76175
35.1%
3 86961
40.0%
4 38603
17.8%
5 6151
 
2.8%
6 2582
 
1.2%
ValueCountFrequency (%)
6 2582
 
1.2%
5 6151
 
2.8%
4 38603
17.8%
3 86961
40.0%
2 76175
35.1%
1 6668
 
3.1%

p5
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6166482
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2024-11-06T16:52:20.818997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q34
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.79022722
Coefficient of variation (CV)0.21849712
Kurtosis0.20887128
Mean3.6166482
Median Absolute Deviation (MAD)0
Skewness-0.49341334
Sum785319
Variance0.62445906
MonotonicityNot monotonic
2024-11-06T16:52:21.001508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 116214
53.5%
3 61620
28.4%
2 18980
 
8.7%
5 18913
 
8.7%
1 1080
 
0.5%
6 333
 
0.2%
ValueCountFrequency (%)
1 1080
 
0.5%
2 18980
 
8.7%
3 61620
28.4%
4 116214
53.5%
5 18913
 
8.7%
6 333
 
0.2%
ValueCountFrequency (%)
6 333
 
0.2%
5 18913
 
8.7%
4 116214
53.5%
3 61620
28.4%
2 18980
 
8.7%
1 1080
 
0.5%

p6
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2267661
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2024-11-06T16:52:21.163078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0381307
Coefficient of variation (CV)0.3217248
Kurtosis-0.37994971
Mean3.2267661
Median Absolute Deviation (MAD)1
Skewness0.31115559
Sum700660
Variance1.0777154
MonotonicityNot monotonic
2024-11-06T16:52:21.373513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 71873
33.1%
4 62361
28.7%
2 56155
25.9%
5 19219
 
8.9%
6 3932
 
1.8%
1 3600
 
1.7%
ValueCountFrequency (%)
1 3600
 
1.7%
2 56155
25.9%
3 71873
33.1%
4 62361
28.7%
5 19219
 
8.9%
6 3932
 
1.8%
ValueCountFrequency (%)
6 3932
 
1.8%
5 19219
 
8.9%
4 62361
28.7%
3 71873
33.1%
2 56155
25.9%
1 3600
 
1.7%

p7
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 MiB
3
109148 
2
75723 
1
32197 
4
 
72

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters217140
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 109148
50.3%
2 75723
34.9%
1 32197
 
14.8%
4 72
 
< 0.1%

Length

2024-11-06T16:52:21.602900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T16:52:21.780425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 109148
50.3%
2 75723
34.9%
1 32197
 
14.8%
4 72
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
3 109148
50.3%
2 75723
34.9%
1 32197
 
14.8%
4 72
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 217140
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 109148
50.3%
2 75723
34.9%
1 32197
 
14.8%
4 72
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 217140
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 109148
50.3%
2 75723
34.9%
1 32197
 
14.8%
4 72
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 217140
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 109148
50.3%
2 75723
34.9%
1 32197
 
14.8%
4 72
 
< 0.1%

p8
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 MiB
3
133831 
2
69203 
1
13885 
4
 
221

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters217140
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 133831
61.6%
2 69203
31.9%
1 13885
 
6.4%
4 221
 
0.1%

Length

2024-11-06T16:52:21.990862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T16:52:22.166393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 133831
61.6%
2 69203
31.9%
1 13885
 
6.4%
4 221
 
0.1%

Most occurring characters

ValueCountFrequency (%)
3 133831
61.6%
2 69203
31.9%
1 13885
 
6.4%
4 221
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 217140
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 133831
61.6%
2 69203
31.9%
1 13885
 
6.4%
4 221
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 217140
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 133831
61.6%
2 69203
31.9%
1 13885
 
6.4%
4 221
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 217140
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 133831
61.6%
2 69203
31.9%
1 13885
 
6.4%
4 221
 
0.1%

p9
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 MiB
2
156886 
1
57316 
3
 
2938

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters217140
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 156886
72.3%
1 57316
 
26.4%
3 2938
 
1.4%

Length

2024-11-06T16:52:22.548371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T16:52:22.723903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 156886
72.3%
1 57316
 
26.4%
3 2938
 
1.4%

Most occurring characters

ValueCountFrequency (%)
2 156886
72.3%
1 57316
 
26.4%
3 2938
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 217140
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 156886
72.3%
1 57316
 
26.4%
3 2938
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 217140
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 156886
72.3%
1 57316
 
26.4%
3 2938
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 217140
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 156886
72.3%
1 57316
 
26.4%
3 2938
 
1.4%

p10
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 MiB
2
150546 
1
61317 
4
 
4953
3
 
324

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters217140
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 150546
69.3%
1 61317
28.2%
4 4953
 
2.3%
3 324
 
0.1%

Length

2024-11-06T16:52:22.917386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T16:52:23.096905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 150546
69.3%
1 61317
28.2%
4 4953
 
2.3%
3 324
 
0.1%

Most occurring characters

ValueCountFrequency (%)
2 150546
69.3%
1 61317
28.2%
4 4953
 
2.3%
3 324
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 217140
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 150546
69.3%
1 61317
28.2%
4 4953
 
2.3%
3 324
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 217140
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 150546
69.3%
1 61317
28.2%
4 4953
 
2.3%
3 324
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 217140
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 150546
69.3%
1 61317
28.2%
4 4953
 
2.3%
3 324
 
0.1%

p11
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0626048
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2024-11-06T16:52:23.273431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.84996755
Coefficient of variation (CV)0.27753093
Kurtosis0.5650499
Mean3.0626048
Median Absolute Deviation (MAD)1
Skewness0.44429864
Sum665014
Variance0.72244484
MonotonicityNot monotonic
2024-11-06T16:52:23.464919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 104495
48.1%
2 51018
23.5%
4 50123
23.1%
5 6733
 
3.1%
1 2658
 
1.2%
6 2113
 
1.0%
ValueCountFrequency (%)
1 2658
 
1.2%
2 51018
23.5%
3 104495
48.1%
4 50123
23.1%
5 6733
 
3.1%
6 2113
 
1.0%
ValueCountFrequency (%)
6 2113
 
1.0%
5 6733
 
3.1%
4 50123
23.1%
3 104495
48.1%
2 51018
23.5%
1 2658
 
1.2%

p12
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9362393
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2024-11-06T16:52:23.618509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median5
Q36
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.143216
Coefficient of variation (CV)0.23159654
Kurtosis-0.54654949
Mean4.9362393
Median Absolute Deviation (MAD)1
Skewness-0.66816962
Sum1071855
Variance1.3069427
MonotonicityNot monotonic
2024-11-06T16:52:23.805010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 95561
44.0%
4 52296
24.1%
5 41840
19.3%
3 21840
 
10.1%
2 4446
 
2.0%
7 756
 
0.3%
1 401
 
0.2%
ValueCountFrequency (%)
1 401
 
0.2%
2 4446
 
2.0%
3 21840
 
10.1%
4 52296
24.1%
5 41840
19.3%
6 95561
44.0%
7 756
 
0.3%
ValueCountFrequency (%)
7 756
 
0.3%
6 95561
44.0%
5 41840
19.3%
4 52296
24.1%
3 21840
 
10.1%
2 4446
 
2.0%
1 401
 
0.2%

p13
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2499401
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2024-11-06T16:52:23.976552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0700319
Coefficient of variation (CV)0.32924664
Kurtosis-0.34886821
Mean3.2499401
Median Absolute Deviation (MAD)1
Skewness0.32346223
Sum705692
Variance1.1449682
MonotonicityNot monotonic
2024-11-06T16:52:24.139117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 89053
41.0%
2 45816
21.1%
4 43246
19.9%
5 29468
 
13.6%
1 6153
 
2.8%
6 3404
 
1.6%
ValueCountFrequency (%)
1 6153
 
2.8%
2 45816
21.1%
3 89053
41.0%
4 43246
19.9%
5 29468
 
13.6%
6 3404
 
1.6%
ValueCountFrequency (%)
6 3404
 
1.6%
5 29468
 
13.6%
4 43246
19.9%
3 89053
41.0%
2 45816
21.1%
1 6153
 
2.8%

p14
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 MiB
3
184971 
2
 
16266
1
 
15678
4
 
225

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters217140
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 184971
85.2%
2 16266
 
7.5%
1 15678
 
7.2%
4 225
 
0.1%

Length

2024-11-06T16:52:24.352545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T16:52:24.536054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 184971
85.2%
2 16266
 
7.5%
1 15678
 
7.2%
4 225
 
0.1%

Most occurring characters

ValueCountFrequency (%)
3 184971
85.2%
2 16266
 
7.5%
1 15678
 
7.2%
4 225
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 217140
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 184971
85.2%
2 16266
 
7.5%
1 15678
 
7.2%
4 225
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 217140
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 184971
85.2%
2 16266
 
7.5%
1 15678
 
7.2%
4 225
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 217140
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 184971
85.2%
2 16266
 
7.5%
1 15678
 
7.2%
4 225
 
0.1%

p15
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 MiB
3
172498 
1
23605 
2
20769 
4
 
268

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters217140
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
3 172498
79.4%
1 23605
 
10.9%
2 20769
 
9.6%
4 268
 
0.1%

Length

2024-11-06T16:52:24.739510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T16:52:24.940973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 172498
79.4%
1 23605
 
10.9%
2 20769
 
9.6%
4 268
 
0.1%

Most occurring characters

ValueCountFrequency (%)
3 172498
79.4%
1 23605
 
10.9%
2 20769
 
9.6%
4 268
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 217140
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 172498
79.4%
1 23605
 
10.9%
2 20769
 
9.6%
4 268
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 217140
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 172498
79.4%
1 23605
 
10.9%
2 20769
 
9.6%
4 268
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 217140
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 172498
79.4%
1 23605
 
10.9%
2 20769
 
9.6%
4 268
 
0.1%

year
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing38255
Missing (%)17.6%
Memory size11.4 MiB
2018.0
77605 
2022.0
53819 
2020.0
47461 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1073310
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018.0
2nd row2018.0
3rd row2018.0
4th row2018.0
5th row2018.0

Common Values

ValueCountFrequency (%)
2018.0 77605
35.7%
2022.0 53819
24.8%
2020.0 47461
21.9%
(Missing) 38255
17.6%

Length

2024-11-06T16:52:25.140439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T16:52:25.335919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2018.0 77605
43.4%
2022.0 53819
30.1%
2020.0 47461
26.5%

Most occurring characters

ValueCountFrequency (%)
0 405231
37.8%
2 333984
31.1%
. 178885
16.7%
1 77605
 
7.2%
8 77605
 
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1073310
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 405231
37.8%
2 333984
31.1%
. 178885
16.7%
1 77605
 
7.2%
8 77605
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1073310
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 405231
37.8%
2 333984
31.1%
. 178885
16.7%
1 77605
 
7.2%
8 77605
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1073310
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 405231
37.8%
2 333984
31.1%
. 178885
16.7%
1 77605
 
7.2%
8 77605
 
7.2%

Interactions

2024-11-06T16:52:06.718714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:30.709037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:33.922442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:36.879532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:39.491544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:42.530416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:45.590230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:48.563277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:51.406674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:54.401661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:57.474441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:00.522288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:03.647928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:06.929151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:30.942412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:34.135870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:37.067029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:39.764814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:42.872500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:45.815628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:48.831560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:51.631072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:54.643015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:57.717791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:00.749680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:03.890279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:07.117649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:31.159831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:34.342318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:37.283455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:39.948323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:43.077950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:46.011106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:49.024045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:51.822560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:54.842481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:57.907284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:00.948149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:04.262292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:07.360997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:31.411158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:34.604616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:37.491893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:40.165740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:43.297364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:46.227527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:49.240467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:52.074885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:55.069873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:58.233411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:01.167562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:04.497655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:07.596365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:31.656501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:34.825027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:37.680388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:40.400114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:43.528745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:46.579585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:49.483815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:52.297290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:55.309236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:58.468782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:01.404927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:04.710087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:07.808798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:31.891872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:35.049427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:37.875866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:40.635484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:43.757135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:46.803983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:49.683282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:52.533657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:55.547595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:58.700163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:01.668224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:04.915536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:08.022228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:32.135221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:35.276817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:38.059376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:40.854897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:43.968596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:47.003450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:49.901699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:52.751076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:55.855770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:58.900626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:01.905588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:05.132957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:08.221694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:32.393530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:35.520166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:38.252859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:41.071318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:44.199950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:47.226853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:50.099170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:52.958522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:56.089147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:59.119046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:02.126997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:05.347381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:08.457064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:32.667797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:35.765510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:38.457311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:41.276769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:44.429336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:47.462224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:50.310603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:53.177934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:56.332496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:59.369373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:02.343418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:05.585743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:08.693431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:32.934084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:35.979938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:38.643812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:41.526102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:44.675676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:47.688618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:50.559937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:53.545950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:56.559887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:59.580808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:02.737364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:05.795187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:08.901874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:33.262207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:36.211318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:38.821337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:41.742524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:44.908056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:47.909027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:50.756412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:53.763370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:56.777306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:59.816178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:02.983704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:06.003628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:09.104333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:33.492591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:36.435719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:39.013823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:42.060671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:45.147415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:48.120464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:50.961863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:53.980788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:56.996720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:00.033598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:03.204114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:06.238996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:09.316765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:33.699038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:36.679067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:39.220270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:42.288064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:45.382785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:48.341872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:51.181276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:54.189228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:51:57.225109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:00.281932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:03.443475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T16:52:06.490324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-06T16:52:25.519423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
conenth_mudingmpiop1p10p11p12p13p14p15p2p3p4p5p6p7p8p9v_selyear
con1.0000.0420.0030.0090.1290.0080.0160.0390.0380.0460.0070.0010.0050.0130.008-0.0090.0000.0110.0200.0050.0100.023
ent0.0421.0000.027-0.0090.333-0.0470.080-0.086-0.061-0.0610.1020.112-0.037-0.057-0.041-0.060-0.0390.1200.1230.1270.0140.052
h_mud0.0030.0271.0000.0000.0190.0110.0120.0150.0130.0150.0160.0170.0170.0160.0180.0100.0140.0130.0140.0180.0070.013
ing0.009-0.0090.0001.0000.001-0.1440.027-0.148-0.053-0.0340.0270.018-0.115-0.145-0.112-0.073-0.0460.0240.0290.0520.0060.019
mpio0.1290.3330.0190.0011.000-0.0040.0480.0130.0320.0840.0430.0520.030-0.0030.034-0.0170.0590.0420.0400.0360.0130.041
p10.008-0.0470.011-0.144-0.0041.0000.2340.4190.2130.2630.1850.1800.4160.7430.4050.4260.2880.3660.3180.2810.0130.082
p100.0160.0800.0120.0270.0480.2341.0000.2360.1090.1320.1870.1900.1720.2230.1700.1420.1210.2760.2560.3640.0090.056
p110.039-0.0860.015-0.1480.0130.4190.2361.0000.2890.3570.1940.2010.4690.4170.4610.3200.3810.2670.2630.3150.0100.052
p120.038-0.0610.013-0.0530.0320.2130.1090.2891.0000.3190.0970.1090.2500.2190.2530.2580.3340.1360.1470.1540.0110.087
p130.046-0.0610.015-0.0340.0840.2630.1320.3570.3191.0000.1230.1370.3230.2770.3250.2920.3930.1640.1720.1940.0090.079
p140.0070.1020.0160.0270.0430.1850.1870.1940.0970.1231.0000.3520.1640.1770.1570.0960.1000.1990.2130.2750.0130.013
p150.0010.1120.0170.0180.0520.1800.1900.2010.1090.1370.3521.0000.1710.1840.1720.1120.1200.2080.2060.2550.0130.014
p20.005-0.0370.017-0.1150.0300.4160.1720.4690.2500.3230.1640.1711.0000.4210.7470.3150.4670.2410.2260.2440.0140.052
p30.013-0.0570.016-0.145-0.0030.7430.2230.4170.2190.2770.1770.1840.4211.0000.4350.4210.2990.3400.3050.2770.0070.082
p40.008-0.0410.018-0.1120.0340.4050.1700.4610.2530.3250.1570.1720.7470.4351.0000.3100.4690.2330.2180.2440.0140.054
p5-0.009-0.0600.010-0.073-0.0170.4260.1420.3200.2580.2920.0960.1120.3150.4210.3101.0000.4280.2190.2070.1700.0140.131
p60.000-0.0390.014-0.0460.0590.2880.1210.3810.3340.3930.1000.1200.4670.2990.4690.4281.0000.1740.1730.1920.0120.060
p70.0110.1200.0130.0240.0420.3660.2760.2670.1360.1640.1990.2080.2410.3400.2330.2190.1741.0000.4060.3310.0120.066
p80.0200.1230.0140.0290.0400.3180.2560.2630.1470.1720.2130.2060.2260.3050.2180.2070.1730.4061.0000.3300.0100.043
p90.0050.1270.0180.0520.0360.2810.3640.3150.1540.1940.2750.2550.2440.2770.2440.1700.1920.3310.3301.0000.0080.036
v_sel0.0100.0140.0070.0060.0130.0130.0090.0100.0110.0090.0130.0130.0140.0070.0140.0140.0120.0120.0100.0081.0000.007
year0.0230.0520.0130.0190.0410.0820.0560.0520.0870.0790.0130.0140.0520.0820.0540.1310.0600.0660.0430.0360.0071.000

Missing values

2024-11-06T16:52:09.775537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-06T16:52:10.796806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-06T16:52:11.979642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

folentconv_seln_hogh_mudi_peringmpioagebfch_defp1p2p3p4p5p6p7p8p9p10p11p12p13p14p15year
011B1671400063101.01300.05025-12018-01-233232323322363312018.0
111B167140006310NaNNaN5025-12018-01-233232323322363312018.0
211B167140006310NaNNaN5025-12018-01-233232323322363312018.0
311B167140006310NaNNaN5025-12018-01-233232323322363312018.0
411B1671400063101.01100.05025-12018-01-233232323322363312018.0
511B167140006310NaNNaN5025-12018-01-233232323322363312018.0
611B1671400064101.0600.05025-12018-01-124243543322464332018.0
711B167140006410NaNNaN5025-12018-01-124243543322464332018.0
811B1671400061102.03500.05025-12018-01-234342443322465332018.0
911B167140006110NaNNaN5025-12018-01-234342443322465332018.0
folentconv_seln_hogh_mudi_peringmpioagebfch_defp1p2p3p4p5p6p7p8p9p10p11p12p13p14p15year
21713012B2123240385310326000.056037-9NaN322222111122232NaN
21713112B2123240385210NaN56037-9NaN343444332246533NaN
21713212B2123240385210NaN56037-9NaN343444332246533NaN
21713312B212324038511028700.056037-9NaN232333111123331NaN
21713412B2123240385410NaN56037-9NaN444233332226333NaN
21713512B2123240385410NaN56037-9NaN444233332226333NaN
21713612B2123240385410NaN56037-9NaN444233332226333NaN
21713712B212324038541024000.056037-9NaN444233332226333NaN
21713812B2123240385310NaN56037-9NaN322222111122232NaN
21713912B2123240385410NaN56037-9NaN444233332226333NaN

Duplicate rows

Most frequently occurring

folentconv_seln_hogh_mudingmpioagebfch_defp1p2p3p4p5p6p7p8p9p10p11p12p13p14p15year# duplicates
2821512A2162240220210NaN14188-6NaN333332322234433NaN18
2889612B1521940562310NaN46099-82018-01-033343443322363332018.016
3369412B1752540415310NaN6031-A2020-03-184343533322363332020.016
3369512B1752540415310NaN6031-ANaN323343332226333NaN16
591311A2021940364310NaN26095-72022-04-082222441221363332022.015
591411A2021940364310NaN26095-72022-04-093333332212333332022.015
960011B154540269110NaN30040-72018-03-124444543321263332018.015
2597112A2022240191410NaN14236-7NaN323223221224333NaN15
2614912A2031440593311NaN39211-1NaN444444332246533NaN15
3369312B1752540415310NaN6031-A2020-01-174232443322242322020.015